Differential neural network identification for homogeneous dynamical systems

Mariana Ballesteros; Andrey Polyakov; Denis Efimov; Isaac Chairez; Alexander Poznyak

doi:10.1016/j.ifacol.2019.11.784

Differential neural network identification for homogeneous dynamical systems

Mariana Ballesteros, Andrey Polyakov, Denis Efimov, Isaac Chairez, Alexander Poznyak

Unidad Profesional Interdisciplinaria de Biotecnología (UPIBI)

Producción científica: Contribución a una revista › Artículo de la conferencia › revisión exhaustiva

5 Citas (Scopus)

Resumen

In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.

Idioma original	Inglés
Páginas (desde-hasta)	233-238
Número de páginas	6
Publicación	IFAC-PapersOnLine
Volumen	52
N.º	16
DOI	https://doi.org/10.1016/j.ifacol.2019.11.784
Estado	Publicada - sep. 2019
Evento	11th IFAC Symposium on Nonlinear Control Systems, NOLCOS 2019 - Vienna, Austria Duración: 4 sep. 2019 → 6 sep. 2019

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abstract = "In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.",

keywords = "Differential neural network, Homogeneous systems, Identification, Nonlinear systems",

author = "Mariana Ballesteros and Andrey Polyakov and Denis Efimov and Isaac Chairez and Alexander Poznyak",

note = "Publisher Copyright: {\textcopyright} 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.; 11th IFAC Symposium on Nonlinear Control Systems, NOLCOS 2019 ; Conference date: 04-09-2019 Through 06-09-2019",

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TY - JOUR

T1 - Differential neural network identification for homogeneous dynamical systems

AU - Ballesteros, Mariana

AU - Polyakov, Andrey

AU - Efimov, Denis

AU - Chairez, Isaac

AU - Poznyak, Alexander

PY - 2019/9

Y1 - 2019/9

N2 - In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.

AB - In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.

KW - Differential neural network

KW - Homogeneous systems

KW - Identification

KW - Nonlinear systems

UR - http://www.scopus.com/inward/record.url?scp=85077463436&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2019.11.784

DO - 10.1016/j.ifacol.2019.11.784

M3 - Artículo de la conferencia

AN - SCOPUS:85077463436

SN - 1474-6670

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SP - 233

EP - 238

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 16

T2 - 11th IFAC Symposium on Nonlinear Control Systems, NOLCOS 2019

Y2 - 4 September 2019 through 6 September 2019

ER -

Differential neural network identification for homogeneous dynamical systems

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